Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations45502
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.6 MiB
Average record size in memory658.0 B

Variable types

Categorical11
Numeric8
Boolean1

Alerts

City is highly overall correlated with monthHigh correlation
day_of_month is highly overall correlated with is_weekend and 1 other fieldsHigh correlation
distance is highly overall correlated with order_hour and 1 other fieldsHigh correlation
is_weekend is highly overall correlated with day_of_month and 1 other fieldsHigh correlation
month is highly overall correlated with City and 1 other fieldsHigh correlation
order_hour is highly overall correlated with distance and 2 other fieldsHigh correlation
order_time_of_day is highly overall correlated with distance and 2 other fieldsHigh correlation
traffic_type is highly overall correlated with order_hour and 1 other fieldsHigh correlation
weekday is highly overall correlated with is_weekendHigh correlation
Festival is highly imbalanced (86.1%) Imbalance
multiple_deliveries has 14061 (30.9%) zeros Zeros

Reproduction

Analysis started2025-06-27 19:24:50.454289
Analysis finished2025-06-27 19:25:04.597725
Duration14.14 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

City
Categorical

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
JAP
3439 
RANCHI
3222 
BANG
3184 
SUR
3182 
HYD
3177 
Other values (17)
29298 

Length

Max length6
Median length3
Mean length3.6604105
Min length3

Characters and Unicode

Total characters166556
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDO
2nd rowBANG
3rd rowBANG
4th rowCOIMB
5th rowCHEN

Common Values

ValueCountFrequency (%)
JAP 3439
 
7.6%
RANCHI 3222
 
7.1%
BANG 3184
 
7.0%
SUR 3182
 
7.0%
HYD 3177
 
7.0%
MUM 3168
 
7.0%
MYS 3164
 
7.0%
COIMB 3162
 
6.9%
VAD 3159
 
6.9%
INDO 3154
 
6.9%
Other values (12) 13491
29.6%

Length

2025-06-27T19:25:04.916953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jap 3439
 
7.6%
ranchi 3222
 
7.1%
bang 3184
 
7.0%
sur 3182
 
7.0%
hyd 3177
 
7.0%
mum 3168
 
7.0%
mys 3164
 
7.0%
coimb 3162
 
6.9%
vad 3159
 
6.9%
indo 3154
 
6.9%
Other values (12) 13491
29.6%

Most occurring characters

ValueCountFrequency (%)
N 16558
 
9.9%
A 15915
 
9.6%
M 12662
 
7.6%
H 12451
 
7.5%
D 10983
 
6.6%
U 10940
 
6.6%
C 10217
 
6.1%
I 9538
 
5.7%
O 8425
 
5.1%
P 7994
 
4.8%
Other values (10) 50873
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 166556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 16558
 
9.9%
A 15915
 
9.6%
M 12662
 
7.6%
H 12451
 
7.5%
D 10983
 
6.6%
U 10940
 
6.6%
C 10217
 
6.1%
I 9538
 
5.7%
O 8425
 
5.1%
P 7994
 
4.8%
Other values (10) 50873
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 166556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 16558
 
9.9%
A 15915
 
9.6%
M 12662
 
7.6%
H 12451
 
7.5%
D 10983
 
6.6%
U 10940
 
6.6%
C 10217
 
6.1%
I 9538
 
5.7%
O 8425
 
5.1%
P 7994
 
4.8%
Other values (10) 50873
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 166556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 16558
 
9.9%
A 15915
 
9.6%
M 12662
 
7.6%
H 12451
 
7.5%
D 10983
 
6.6%
U 10940
 
6.6%
C 10217
 
6.1%
I 9538
 
5.7%
O 8425
 
5.1%
P 7994
 
4.8%
Other values (10) 50873
30.5%

Rider_age
Real number (ℝ)

Distinct83
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.555009
Minimum20
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.6 KiB
2025-06-27T19:25:05.043598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21
Q125
median30
Q334
95-th percentile38
Maximum39
Range19
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.6689428
Coefficient of variation (CV)0.19180989
Kurtosis-1.1608058
Mean29.555009
Median Absolute Deviation (MAD)5
Skewness-0.014987418
Sum1344812
Variance32.136912
MonotonicityNot monotonic
2025-06-27T19:25:05.187681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 2293
 
5.0%
35 2270
 
5.0%
36 2266
 
5.0%
29 2246
 
4.9%
32 2230
 
4.9%
37 2228
 
4.9%
38 2220
 
4.9%
28 2217
 
4.9%
24 2217
 
4.9%
33 2213
 
4.9%
Other values (73) 23102
50.8%
ValueCountFrequency (%)
20 2136
4.7%
20.8 1
 
< 0.1%
21 2153
4.7%
21.2 1
 
< 0.1%
21.6 1
 
< 0.1%
21.8 2
 
< 0.1%
22 2197
4.8%
22.2 3
 
< 0.1%
22.4 3
 
< 0.1%
22.6 3
 
< 0.1%
ValueCountFrequency (%)
39 2144
4.7%
38 2220
4.9%
37 2228
4.9%
36.6 1
 
< 0.1%
36.4 3
 
< 0.1%
36.2 2
 
< 0.1%
36 2266
5.0%
35.8 2
 
< 0.1%
35.6 4
 
< 0.1%
35.4 2
 
< 0.1%

Rider_rating
Real number (ℝ)

Distinct108
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.635867
Minimum2.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.6 KiB
2025-06-27T19:25:05.331318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile4
Q14.5
median4.7
Q34.8
95-th percentile5
Maximum5
Range2.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.30883104
Coefficient of variation (CV)0.066617752
Kurtosis5.3598211
Mean4.635867
Median Absolute Deviation (MAD)0.16
Skewness-1.8166234
Sum210941.22
Variance0.09537661
MonotonicityNot monotonic
2025-06-27T19:25:05.478238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.7 7249
15.9%
4.8 7228
15.9%
4.9 7055
15.5%
4.6 7012
15.4%
5 3996
8.8%
4.5 3342
7.3%
4.1 1433
 
3.1%
4.2 1422
 
3.1%
4.3 1414
 
3.1%
4.4 1373
 
3.0%
Other values (98) 3978
8.7%
ValueCountFrequency (%)
2.5 20
< 0.1%
2.6 22
< 0.1%
2.7 22
< 0.1%
2.8 19
< 0.1%
2.9 19
< 0.1%
2.94 1
 
< 0.1%
3 6
 
< 0.1%
3.1 29
0.1%
3.2 29
0.1%
3.3 25
0.1%
ValueCountFrequency (%)
5 3996
8.8%
4.96 1
 
< 0.1%
4.94 1
 
< 0.1%
4.92 4
 
< 0.1%
4.9 7055
15.5%
4.88 8
 
< 0.1%
4.88 16
 
< 0.1%
4.86 5
 
< 0.1%
4.86 19
 
< 0.1%
4.86 12
 
< 0.1%

pickup_time
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9947695
Minimum5
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.6 KiB
2025-06-27T19:25:05.589733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q15
median10
Q315
95-th percentile15
Maximum15
Range10
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.0283352
Coefficient of variation (CV)0.40304433
Kurtosis-1.4666526
Mean9.9947695
Median Absolute Deviation (MAD)5
Skewness0.00043647038
Sum454782
Variance16.227484
MonotonicityNot monotonic
2025-06-27T19:25:05.685774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 14903
32.8%
5 14714
32.3%
15 14614
32.1%
11 328
 
0.7%
9 278
 
0.6%
12 208
 
0.5%
8 183
 
0.4%
13 114
 
0.3%
7 84
 
0.2%
14 47
 
0.1%
ValueCountFrequency (%)
5 14714
32.3%
6 29
 
0.1%
7 84
 
0.2%
8 183
 
0.4%
9 278
 
0.6%
10 14903
32.8%
11 328
 
0.7%
12 208
 
0.5%
13 114
 
0.3%
14 47
 
0.1%
ValueCountFrequency (%)
15 14614
32.1%
14 47
 
0.1%
13 114
 
0.3%
12 208
 
0.5%
11 328
 
0.7%
10 14903
32.8%
9 278
 
0.6%
8 183
 
0.4%
7 84
 
0.2%
6 29
 
0.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Fog
7770 
Sandstorms
7759 
Stormy
7710 
Cloudy
7542 
Windy
7422 

Length

Max length10
Median length6
Mean length5.8462705
Min length3

Characters and Unicode

Total characters266017
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunny
2nd rowStormy
3rd rowSandstorms
4th rowSunny
5th rowCloudy

Common Values

ValueCountFrequency (%)
Fog 7770
17.1%
Sandstorms 7759
17.1%
Stormy 7710
16.9%
Cloudy 7542
16.6%
Windy 7422
16.3%
Sunny 7299
16.0%

Length

2025-06-27T19:25:05.818830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:05.912620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fog 7770
17.1%
sandstorms 7759
17.1%
stormy 7710
16.9%
cloudy 7542
16.6%
windy 7422
16.3%
sunny 7299
16.0%

Most occurring characters

ValueCountFrequency (%)
o 30781
11.6%
y 29973
11.3%
n 29779
11.2%
S 22768
8.6%
d 22723
8.5%
s 15518
 
5.8%
m 15469
 
5.8%
r 15469
 
5.8%
t 15469
 
5.8%
u 14841
 
5.6%
Other values (7) 53227
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 266017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 30781
11.6%
y 29973
11.3%
n 29779
11.2%
S 22768
8.6%
d 22723
8.5%
s 15518
 
5.8%
m 15469
 
5.8%
r 15469
 
5.8%
t 15469
 
5.8%
u 14841
 
5.6%
Other values (7) 53227
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 266017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 30781
11.6%
y 29973
11.3%
n 29779
11.2%
S 22768
8.6%
d 22723
8.5%
s 15518
 
5.8%
m 15469
 
5.8%
r 15469
 
5.8%
t 15469
 
5.8%
u 14841
 
5.6%
Other values (7) 53227
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 266017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 30781
11.6%
y 29973
11.3%
n 29779
11.2%
S 22768
8.6%
d 22723
8.5%
s 15518
 
5.8%
m 15469
 
5.8%
r 15469
 
5.8%
t 15469
 
5.8%
u 14841
 
5.6%
Other values (7) 53227
20.0%

traffic_type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
low
15635 
jam
14486 
medium
10947 
high
4434 

Length

Max length6
Median length3
Mean length3.8191948
Min length3

Characters and Unicode

Total characters173781
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh
2nd rowjam
3rd rowlow
4th rowmedium
5th rowhigh

Common Values

ValueCountFrequency (%)
low 15635
34.4%
jam 14486
31.8%
medium 10947
24.1%
high 4434
 
9.7%

Length

2025-06-27T19:25:06.049078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:06.142950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 15635
34.4%
jam 14486
31.8%
medium 10947
24.1%
high 4434
 
9.7%

Most occurring characters

ValueCountFrequency (%)
m 36380
20.9%
l 15635
9.0%
w 15635
9.0%
o 15635
9.0%
i 15381
8.9%
j 14486
 
8.3%
a 14486
 
8.3%
e 10947
 
6.3%
d 10947
 
6.3%
u 10947
 
6.3%
Other values (2) 13302
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 36380
20.9%
l 15635
9.0%
w 15635
9.0%
o 15635
9.0%
i 15381
8.9%
j 14486
 
8.3%
a 14486
 
8.3%
e 10947
 
6.3%
d 10947
 
6.3%
u 10947
 
6.3%
Other values (2) 13302
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 36380
20.9%
l 15635
9.0%
w 15635
9.0%
o 15635
9.0%
i 15381
8.9%
j 14486
 
8.3%
a 14486
 
8.3%
e 10947
 
6.3%
d 10947
 
6.3%
u 10947
 
6.3%
Other values (2) 13302
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 36380
20.9%
l 15635
9.0%
w 15635
9.0%
o 15635
9.0%
i 15381
8.9%
j 14486
 
8.3%
a 14486
 
8.3%
e 10947
 
6.3%
d 10947
 
6.3%
u 10947
 
6.3%
Other values (2) 13302
 
7.7%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2
15034 
1
15030 
0
15009 
3
 
429

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45502
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
33.0%
3 429
 
0.9%

Length

2025-06-27T19:25:06.245358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:06.323519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
33.0%
3 429
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
33.0%
3 429
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
33.0%
3 429
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
33.0%
3 429
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
33.0%
3 429
 
0.9%

Type_of_order
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Snack
11512 
Meal
11435 
Drinks
11294 
Buffet
11261 

Length

Max length7
Median length6
Mean length6.2443849
Min length5

Characters and Unicode

Total characters284132
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSnack
2nd rowSnack
3rd rowDrinks
4th rowBuffet
5th rowSnack

Common Values

ValueCountFrequency (%)
Snack 11512
25.3%
Meal 11435
25.1%
Drinks 11294
24.8%
Buffet 11261
24.7%

Length

2025-06-27T19:25:06.444084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:06.743128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
snack 11512
25.3%
meal 11435
25.1%
drinks 11294
24.8%
buffet 11261
24.7%

Most occurring characters

ValueCountFrequency (%)
45502
16.0%
a 22947
 
8.1%
k 22806
 
8.0%
n 22806
 
8.0%
e 22696
 
8.0%
f 22522
 
7.9%
S 11512
 
4.1%
c 11512
 
4.1%
M 11435
 
4.0%
l 11435
 
4.0%
Other values (7) 78959
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
45502
16.0%
a 22947
 
8.1%
k 22806
 
8.0%
n 22806
 
8.0%
e 22696
 
8.0%
f 22522
 
7.9%
S 11512
 
4.1%
c 11512
 
4.1%
M 11435
 
4.0%
l 11435
 
4.0%
Other values (7) 78959
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
45502
16.0%
a 22947
 
8.1%
k 22806
 
8.0%
n 22806
 
8.0%
e 22696
 
8.0%
f 22522
 
7.9%
S 11512
 
4.1%
c 11512
 
4.1%
M 11435
 
4.0%
l 11435
 
4.0%
Other values (7) 78959
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
45502
16.0%
a 22947
 
8.1%
k 22806
 
8.0%
n 22806
 
8.0%
e 22696
 
8.0%
f 22522
 
7.9%
S 11512
 
4.1%
c 11512
 
4.1%
M 11435
 
4.0%
l 11435
 
4.0%
Other values (7) 78959
27.8%

Type_of_vehicle
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
motorcycle
26427 
scooter
15244 
electric_scooter
3778 
bicycle
 
53

Length

Max length16
Median length10
Mean length9.4896268
Min length7

Characters and Unicode

Total characters431797
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmotorcycle
2nd rowscooter
3rd rowmotorcycle
4th rowmotorcycle
5th rowscooter

Common Values

ValueCountFrequency (%)
motorcycle 26427
58.1%
scooter 15244
33.5%
electric_scooter 3778
 
8.3%
bicycle 53
 
0.1%

Length

2025-06-27T19:25:06.855160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:06.932652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
motorcycle 26427
58.1%
scooter 15244
33.5%
electric_scooter 3778
 
8.3%
bicycle 53
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 90898
21.1%
c 79538
18.4%
e 53058
12.3%
t 49227
11.4%
r 49227
11.4%
l 30258
 
7.0%
y 26480
 
6.1%
m 26427
 
6.1%
s 19022
 
4.4%
i 3831
 
0.9%
Other values (2) 3831
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 431797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 90898
21.1%
c 79538
18.4%
e 53058
12.3%
t 49227
11.4%
r 49227
11.4%
l 30258
 
7.0%
y 26480
 
6.1%
m 26427
 
6.1%
s 19022
 
4.4%
i 3831
 
0.9%
Other values (2) 3831
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 431797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 90898
21.1%
c 79538
18.4%
e 53058
12.3%
t 49227
11.4%
r 49227
11.4%
l 30258
 
7.0%
y 26480
 
6.1%
m 26427
 
6.1%
s 19022
 
4.4%
i 3831
 
0.9%
Other values (2) 3831
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 431797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 90898
21.1%
c 79538
18.4%
e 53058
12.3%
t 49227
11.4%
r 49227
11.4%
l 30258
 
7.0%
y 26480
 
6.1%
m 26427
 
6.1%
s 19022
 
4.4%
i 3831
 
0.9%
Other values (2) 3831
 
0.9%

multiple_deliveries
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74418707
Minimum0
Maximum3
Zeros14061
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size355.6 KiB
2025-06-27T19:25:07.014997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.56720358
Coefficient of variation (CV)0.76217877
Kurtosis0.97546509
Mean0.74418707
Median Absolute Deviation (MAD)0
Skewness0.33071899
Sum33862
Variance0.32171991
MonotonicityNot monotonic
2025-06-27T19:25:07.104854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 28242
62.1%
0 14061
30.9%
2 1983
 
4.4%
3 361
 
0.8%
0.6 346
 
0.8%
0.8 328
 
0.7%
0.4 124
 
0.3%
1.2 30
 
0.1%
0.2 19
 
< 0.1%
1.4 7
 
< 0.1%
ValueCountFrequency (%)
0 14061
30.9%
0.2 19
 
< 0.1%
0.4 124
 
0.3%
0.6 346
 
0.8%
0.8 328
 
0.7%
1 28242
62.1%
1.2 30
 
0.1%
1.4 7
 
< 0.1%
1.8 1
 
< 0.1%
2 1983
 
4.4%
ValueCountFrequency (%)
3 361
 
0.8%
2 1983
 
4.4%
1.8 1
 
< 0.1%
1.4 7
 
< 0.1%
1.2 30
 
0.1%
1 28242
62.1%
0.8 328
 
0.7%
0.6 346
 
0.8%
0.4 124
 
0.3%
0.2 19
 
< 0.1%

Festival
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
False
44608 
True
 
894
ValueCountFrequency (%)
False 44608
98.0%
True 894
 
2.0%
2025-06-27T19:25:07.178051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

City_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
metropolitian
35200 
urban
10111 
semi-urban
 
191

Length

Max length13
Median length13
Mean length11.209727
Min length5

Characters and Unicode

Total characters510065
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurban
2nd rowmetropolitian
3rd rowurban
4th rowmetropolitian
5th rowmetropolitian

Common Values

ValueCountFrequency (%)
metropolitian 35200
77.4%
urban 10111
 
22.2%
semi-urban 191
 
0.4%

Length

2025-06-27T19:25:07.270698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:07.351931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metropolitian 35200
77.4%
urban 10111
 
22.2%
semi-urban 191
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 70591
13.8%
o 70400
13.8%
t 70400
13.8%
r 45502
8.9%
a 45502
8.9%
n 45502
8.9%
m 35391
6.9%
e 35391
6.9%
p 35200
6.9%
l 35200
6.9%
Other values (4) 20986
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 510065
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 70591
13.8%
o 70400
13.8%
t 70400
13.8%
r 45502
8.9%
a 45502
8.9%
n 45502
8.9%
m 35391
6.9%
e 35391
6.9%
p 35200
6.9%
l 35200
6.9%
Other values (4) 20986
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 510065
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 70591
13.8%
o 70400
13.8%
t 70400
13.8%
r 45502
8.9%
a 45502
8.9%
n 45502
8.9%
m 35391
6.9%
e 35391
6.9%
p 35200
6.9%
l 35200
6.9%
Other values (4) 20986
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 510065
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 70591
13.8%
o 70400
13.8%
t 70400
13.8%
r 45502
8.9%
a 45502
8.9%
n 45502
8.9%
m 35391
6.9%
e 35391
6.9%
p 35200
6.9%
l 35200
6.9%
Other values (4) 20986
 
4.1%

time_taken
Real number (ℝ)

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.297591
Minimum10
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.6 KiB
2025-06-27T19:25:07.450272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q119
median26
Q332
95-th percentile44
Maximum54
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.386419
Coefficient of variation (CV)0.35693075
Kurtosis-0.31163775
Mean26.297591
Median Absolute Deviation (MAD)7
Skewness0.48558247
Sum1196593
Variance88.104862
MonotonicityNot monotonic
2025-06-27T19:25:07.595046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
26 2121
 
4.7%
25 2044
 
4.5%
27 1970
 
4.3%
28 1956
 
4.3%
29 1954
 
4.3%
19 1819
 
4.0%
15 1805
 
4.0%
18 1763
 
3.9%
16 1704
 
3.7%
17 1690
 
3.7%
Other values (35) 26676
58.6%
ValueCountFrequency (%)
10 750
1.6%
11 756
1.7%
12 745
1.6%
13 715
 
1.6%
14 736
1.6%
15 1805
4.0%
16 1704
3.7%
17 1690
3.7%
18 1763
3.9%
19 1819
4.0%
ValueCountFrequency (%)
54 91
 
0.2%
53 100
 
0.2%
52 79
 
0.2%
51 94
 
0.2%
50 72
 
0.2%
49 280
0.6%
48 276
0.6%
47 295
0.6%
46 274
0.6%
45 241
0.5%

weekday
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Wednesday
7077 
Friday
7020 
Tuesday
6363 
Thursday
6338 
Saturday
6276 
Other values (2)
12428 

Length

Max length9
Median length8
Mean length7.160872
Min length6

Characters and Unicode

Total characters325834
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowFriday
3rd rowSaturday
4th rowTuesday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Wednesday 7077
15.6%
Friday 7020
15.4%
Tuesday 6363
14.0%
Thursday 6338
13.9%
Saturday 6276
13.8%
Sunday 6231
13.7%
Monday 6197
13.6%

Length

2025-06-27T19:25:07.719670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:07.840474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
wednesday 7077
15.6%
friday 7020
15.4%
tuesday 6363
14.0%
thursday 6338
13.9%
saturday 6276
13.8%
sunday 6231
13.7%
monday 6197
13.6%

Most occurring characters

ValueCountFrequency (%)
d 52579
16.1%
a 51778
15.9%
y 45502
14.0%
u 25208
7.7%
e 20517
 
6.3%
s 19778
 
6.1%
r 19634
 
6.0%
n 19505
 
6.0%
T 12701
 
3.9%
S 12507
 
3.8%
Other values (7) 46125
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 325834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 52579
16.1%
a 51778
15.9%
y 45502
14.0%
u 25208
7.7%
e 20517
 
6.3%
s 19778
 
6.1%
r 19634
 
6.0%
n 19505
 
6.0%
T 12701
 
3.9%
S 12507
 
3.8%
Other values (7) 46125
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 325834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 52579
16.1%
a 51778
15.9%
y 45502
14.0%
u 25208
7.7%
e 20517
 
6.3%
s 19778
 
6.1%
r 19634
 
6.0%
n 19505
 
6.0%
T 12701
 
3.9%
S 12507
 
3.8%
Other values (7) 46125
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 325834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 52579
16.1%
a 51778
15.9%
y 45502
14.0%
u 25208
7.7%
e 20517
 
6.3%
s 19778
 
6.1%
r 19634
 
6.0%
n 19505
 
6.0%
T 12701
 
3.9%
S 12507
 
3.8%
Other values (7) 46125
14.2%

is_weekend
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
32995 
1
12507 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45502
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 32995
72.5%
1 12507
 
27.5%

Length

2025-06-27T19:25:07.978359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:08.047154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 32995
72.5%
1 12507
 
27.5%

Most occurring characters

ValueCountFrequency (%)
0 32995
72.5%
1 12507
 
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32995
72.5%
1 12507
 
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32995
72.5%
1 12507
 
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32995
72.5%
1 12507
 
27.5%

day_of_month
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.811657
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.6 KiB
2025-06-27T19:25:08.136064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median13
Q320
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.7095401
Coefficient of variation (CV)0.63059344
Kurtosis-0.9700908
Mean13.811657
Median Absolute Deviation (MAD)7
Skewness0.29909432
Sum628458
Variance75.856089
MonotonicityNot monotonic
2025-06-27T19:25:08.275363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3 2323
 
5.1%
5 2308
 
5.1%
1 2268
 
5.0%
15 2134
 
4.7%
13 2120
 
4.7%
11 2115
 
4.6%
17 2070
 
4.5%
2 1999
 
4.4%
6 1941
 
4.3%
4 1921
 
4.2%
Other values (20) 24303
53.4%
ValueCountFrequency (%)
1 2268
5.0%
2 1999
4.4%
3 2323
5.1%
4 1921
4.2%
5 2308
5.1%
6 1941
4.3%
7 1153
2.5%
8 962
2.1%
9 1156
2.5%
10 996
2.2%
ValueCountFrequency (%)
31 966
2.1%
30 1139
2.5%
29 975
2.1%
28 1139
2.5%
27 962
2.1%
26 1166
2.6%
25 974
2.1%
24 1159
2.5%
23 963
2.1%
21 1144
2.5%

month
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
3
31919 
2
7230 
4
6353 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45502
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 31919
70.1%
2 7230
 
15.9%
4 6353
 
14.0%

Length

2025-06-27T19:25:08.403181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:08.679649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 31919
70.1%
2 7230
 
15.9%
4 6353
 
14.0%

Most occurring characters

ValueCountFrequency (%)
3 31919
70.1%
2 7230
 
15.9%
4 6353
 
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 31919
70.1%
2 7230
 
15.9%
4 6353
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 31919
70.1%
2 7230
 
15.9%
4 6353
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 31919
70.1%
2 7230
 
15.9%
4 6353
 
14.0%

order_hour
Real number (ℝ)

High correlation 

Distinct74
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.427942
Minimum0
Maximum23
Zeros430
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size355.6 KiB
2025-06-27T19:25:08.793256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q115
median19
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7596781
Coefficient of variation (CV)0.27310615
Kurtosis0.51770754
Mean17.427942
Median Absolute Deviation (MAD)2
Skewness-1.0309661
Sum793006.2
Variance22.654536
MonotonicityNot monotonic
2025-06-27T19:25:08.946156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 4693
10.3%
20 4647
10.2%
19 4611
10.1%
22 4577
10.1%
23 4511
9.9%
18 4506
9.9%
17 4363
9.6%
10 1996
 
4.4%
11 1968
 
4.3%
9 1948
 
4.3%
Other values (64) 7682
16.9%
ValueCountFrequency (%)
0 430
 
0.9%
6.8 3
 
< 0.1%
7 1
 
< 0.1%
8 1818
4.0%
8.2 1
 
< 0.1%
8.6 1
 
< 0.1%
9 1948
4.3%
9.2 1
 
< 0.1%
9.4 4
 
< 0.1%
9.6 1
 
< 0.1%
ValueCountFrequency (%)
23 4511
9.9%
22.8 5
 
< 0.1%
22.6 12
 
< 0.1%
22.4 13
 
< 0.1%
22.2 6
 
< 0.1%
22 4577
10.1%
21.8 1
 
< 0.1%
21.6 1
 
< 0.1%
21.4 3
 
< 0.1%
21.2 6
 
< 0.1%

order_time_of_day
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Evening
24006 
Afternoon
12437 
Morning
9059 

Length

Max length9
Median length7
Mean length7.5466573
Min length7

Characters and Unicode

Total characters343388
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorning
2nd rowEvening
3rd rowMorning
4th rowAfternoon
5th rowAfternoon

Common Values

ValueCountFrequency (%)
Evening 24006
52.8%
Afternoon 12437
27.3%
Morning 9059
 
19.9%

Length

2025-06-27T19:25:09.077227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-27T19:25:09.166716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
evening 24006
52.8%
afternoon 12437
27.3%
morning 9059
 
19.9%

Most occurring characters

ValueCountFrequency (%)
n 91004
26.5%
e 36443
10.6%
o 33933
 
9.9%
i 33065
 
9.6%
g 33065
 
9.6%
v 24006
 
7.0%
E 24006
 
7.0%
r 21496
 
6.3%
f 12437
 
3.6%
A 12437
 
3.6%
Other values (2) 21496
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 343388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 91004
26.5%
e 36443
10.6%
o 33933
 
9.9%
i 33065
 
9.6%
g 33065
 
9.6%
v 24006
 
7.0%
E 24006
 
7.0%
r 21496
 
6.3%
f 12437
 
3.6%
A 12437
 
3.6%
Other values (2) 21496
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 343388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 91004
26.5%
e 36443
10.6%
o 33933
 
9.9%
i 33065
 
9.6%
g 33065
 
9.6%
v 24006
 
7.0%
E 24006
 
7.0%
r 21496
 
6.3%
f 12437
 
3.6%
A 12437
 
3.6%
Other values (2) 21496
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 343388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 91004
26.5%
e 36443
10.6%
o 33933
 
9.9%
i 33065
 
9.6%
g 33065
 
9.6%
v 24006
 
7.0%
E 24006
 
7.0%
r 21496
 
6.3%
f 12437
 
3.6%
A 12437
 
3.6%
Other values (2) 21496
 
6.3%

distance
Real number (ℝ)

High correlation 

Distinct7821
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.721955
Minimum1.4650674
Maximum20.969489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.6 KiB
2025-06-27T19:25:09.277272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.4650674
5-th percentile1.5367188
Q14.6819428
median9.2203731
Q313.583332
95-th percentile19.914689
Maximum20.969489
Range19.504422
Interquartile range (IQR)8.9013897

Descriptive statistics

Standard deviation5.4334969
Coefficient of variation (CV)0.55888933
Kurtosis-0.80555382
Mean9.721955
Median Absolute Deviation (MAD)4.3630771
Skewness0.32820515
Sum442368.4
Variance29.522889
MonotonicityNot monotonic
2025-06-27T19:25:09.424650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.68204453 18
 
< 0.1%
16.85211869 18
 
< 0.1%
19.8756806 18
 
< 0.1%
1.509479521 18
 
< 0.1%
12.29416638 18
 
< 0.1%
6.210607623 18
 
< 0.1%
2.979795885 18
 
< 0.1%
9.32660324 18
 
< 0.1%
13.58430606 18
 
< 0.1%
7.573523734 18
 
< 0.1%
Other values (7811) 45322
99.6%
ValueCountFrequency (%)
1.465067405 3
< 0.1%
1.465122557 4
< 0.1%
1.465140019 3
< 0.1%
1.465158713 4
< 0.1%
1.465161362 4
< 0.1%
1.465185811 4
< 0.1%
1.46518979 3
< 0.1%
1.465201601 3
< 0.1%
1.46520251 2
< 0.1%
1.465202575 3
< 0.1%
ValueCountFrequency (%)
20.96948938 3
< 0.1%
20.96904516 3
< 0.1%
20.96902845 3
< 0.1%
20.96899356 4
< 0.1%
20.96899262 3
< 0.1%
20.96898885 1
 
< 0.1%
20.96889945 4
< 0.1%
20.96888933 4
< 0.1%
20.96883412 3
< 0.1%
20.96874894 2
< 0.1%

Interactions

2025-06-27T19:25:02.820125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:54.608736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:55.597842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:56.737662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:57.608762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:58.875326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:00.497855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:01.958955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:02.947625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:54.727130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:55.723074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:56.849463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:57.735976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:59.046785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:00.683799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:02.067511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:03.074166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:54.839718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:55.834662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:56.952912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:57.856277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:59.212533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:00.854832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:02.177372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:03.197406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:54.951016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:55.943583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:57.051094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:57.970731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:59.403030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:01.026828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:02.278089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:03.343195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:55.075834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:56.063256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:57.163819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:58.094554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:59.753736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:01.213477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:02.383806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:03.681588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:55.216229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:56.170938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:57.270776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:58.210659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:00.023593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:01.382958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:02.489775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:03.802031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:55.350969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:56.299006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:57.388853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:58.544379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:00.174411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:01.516594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:02.610228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:03.908728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:55.469672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:56.618818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:57.492951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:24:58.686450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:00.314334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:01.840831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-27T19:25:02.707844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-27T19:25:09.567808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CityCity_typeFestivalRider_ageRider_ratingType_of_orderType_of_vehicleVehicle_conditionWeatherconditionsday_of_monthdistanceis_weekendmonthmultiple_deliveriesorder_hourorder_time_of_daypickup_timetime_takentraffic_typeweekday
City1.0000.0000.0000.0070.0030.0000.0000.0000.0000.1890.1180.0110.7070.0060.0040.0070.0000.0080.0000.046
City_type0.0001.0000.0960.0590.0510.0070.0340.0580.0350.0080.0550.0000.0000.1210.0600.0440.0200.2570.0690.017
Festival0.0000.0961.0000.0710.0700.0000.0560.1000.0720.0320.1060.0000.0000.2100.0920.0730.0060.4250.1260.020
Rider_age0.0070.0590.0711.000-0.1030.0000.0160.0530.0120.004-0.0020.0090.0000.1130.0020.012-0.0080.3080.0000.000
Rider_rating0.0030.0510.070-0.1031.0000.0050.0550.0930.088-0.004-0.0700.0000.000-0.094-0.0250.0700.001-0.2910.0830.015
Type_of_order0.0000.0070.0000.0000.0051.0000.0000.0050.0010.0000.0080.0050.0000.0060.0000.0000.0000.0000.0000.000
Type_of_vehicle0.0000.0340.0560.0160.0550.0001.0000.4540.0250.0030.0070.0000.0000.0460.0200.0100.1060.1050.0170.007
Vehicle_condition0.0000.0580.1000.0530.0930.0050.4541.0000.0600.0020.0040.0020.0000.0750.0510.0380.2560.1820.0480.002
Weatherconditions0.0000.0350.0720.0120.0880.0010.0250.0601.0000.0050.0000.0000.0000.0530.0050.0000.0270.1380.0100.000
day_of_month0.1890.0080.0320.004-0.0040.0000.0030.0020.0051.0000.0530.5380.5740.0150.0330.085-0.0020.0240.0550.422
distance0.1180.0550.106-0.002-0.0700.0080.0070.0040.0000.0531.0000.0000.0410.1230.5170.6580.0020.3120.4240.087
is_weekend0.0110.0000.0000.0090.0000.0050.0000.0020.0000.5380.0001.0000.0600.0000.0070.0000.0000.0000.0001.000
month0.7070.0000.0000.0000.0000.0000.0000.0000.0000.5740.0410.0601.0000.0000.0050.0020.0040.0100.0020.151
multiple_deliveries0.0060.1210.2100.113-0.0940.0060.0460.0750.0530.0150.1230.0000.0001.0000.0390.074-0.0020.3370.1090.021
order_hour0.0040.0600.0920.002-0.0250.0000.0200.0510.0050.0330.5170.0070.0050.0391.0000.9620.0040.1050.8030.055
order_time_of_day0.0070.0440.0730.0120.0700.0000.0100.0380.0000.0850.6580.0000.0020.0740.9621.0000.0550.1860.7570.080
pickup_time0.0000.0200.006-0.0080.0010.0000.1060.2560.027-0.0020.0020.0000.004-0.0020.0040.0551.000-0.0070.0250.000
time_taken0.0080.2570.4250.308-0.2910.0000.1050.1820.1380.0240.3120.0000.0100.3370.1050.186-0.0071.0000.2630.038
traffic_type0.0000.0690.1260.0000.0830.0000.0170.0480.0100.0550.4240.0000.0020.1090.8030.7570.0250.2631.0000.053
weekday0.0460.0170.0200.0000.0150.0000.0070.0020.0000.4220.0871.0000.1510.0210.0550.0800.0000.0380.0531.000

Missing values

2025-06-27T19:25:04.122058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-27T19:25:04.383776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CityRider_ageRider_ratingpickup_timeWeatherconditionstraffic_typeVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCity_typetime_takenweekdayis_weekendday_of_monthmonthorder_hourorder_time_of_daydistance
0INDO37.04.915.0Sunnyhigh2Snackmotorcycle0.0nourban24Saturday119311.0Morning3.025149
1BANG34.04.55.0Stormyjam2Snackscooter1.0nometropolitian33Friday025319.0Evening20.183530
2BANG23.04.415.0Sandstormslow0Drinksmotorcycle1.0nourban26Saturday11938.0Morning1.552758
3COIMB38.04.710.0Sunnymedium0Buffetmotorcycle1.0nometropolitian21Tuesday05418.0Afternoon7.790401
4CHEN32.04.615.0Cloudyhigh1Snackscooter1.0nometropolitian30Saturday126313.0Afternoon6.210138
5HYD22.04.810.0Cloudyjam0Buffetmotorcycle1.0nourban26Friday011321.0Evening4.610365
6RANCHI33.04.715.0Fogjam1Mealscooter1.0nometropolitian40Friday04319.0Evening16.600361
7MYS35.04.65.0Cloudymedium2Mealmotorcycle1.0nometropolitian32Monday014317.0Afternoon20.205253
8HYD22.04.810.0Stormyjam0Buffetmotorcycle1.0nometropolitian34Sunday120320.0Evening19.975520
9DEH36.04.215.0Fogjam2Snackmotorcycle3.0nometropolitian46Saturday112221.0Evening10.280582
CityRider_ageRider_ratingpickup_timeWeatherconditionstraffic_typeVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCity_typetime_takenweekdayis_weekendday_of_monthmonthorder_hourorder_time_of_daydistance
45492MYS36.04.810.0Sunnyjam2Drinkselectric_scooter1.0nourban29Friday018321.0Evening20.206808
45493CHEN37.04.815.0Sandstormslow2Drinkselectric_scooter0.0nometropolitian20Tuesday0549.0Morning3.104827
45494KNP30.04.215.0Cloudymedium1Snackmotorcycle2.0yesmetropolitian42Monday014218.0Afternoon10.445325
45495BANG28.04.95.0Sandstormsjam1Mealscooter1.0nometropolitian29Wednesday030321.0Evening4.657133
45496RANCHI35.04.210.0Windyjam2Drinksmotorcycle1.0nometropolitian33Tuesday08321.0Evening16.600272
45497JAP30.04.810.0Windyhigh1Mealmotorcycle0.0nometropolitian32Thursday024311.0Morning1.489846
45498AGR21.04.615.0Windyjam0Buffetmotorcycle1.0nometropolitian36Wednesday016219.0Evening11.002120
45499CHEN30.04.915.0Cloudylow1Drinksscooter0.0nometropolitian16Friday011323.0Evening4.657195
45500COIMB20.04.75.0Cloudyhigh0Snackmotorcycle1.0nometropolitian26Monday07313.0Afternoon6.232393
45501RANCHI23.04.95.0Fogmedium2Snackscooter1.0nometropolitian36Wednesday02317.0Afternoon12.074396